1. Technological Field
The present disclosure relates generally to artificial neural networks, and more particularly in one exemplary aspect to computer apparatus and methods for plasticity implementation in a pulse-code neural network.
2. Description of Related Art
Artificial spiking neural networks are frequently used to gain an understanding of biological neural networks, and for solving artificial intelligence problems. These networks typically employ a pulse-coded mechanism, which encodes information using timing of the pulses. Such pulses (also referred to as “spikes” or ‘impulses’) are short-lasting (typically on the order of 1-2 ms) discrete temporal events. Several exemplary embodiments of such encoding are described in commonly owned and co-pending U.S. patent application Ser. No. 13/152,084 entitled APPARATUS AND METHODS FOR PULSE-CODE INVARIANT OBJECT RECOGNITION”, filed Jun. 2, 2011, the foregoing being incorporated herein by reference in its entirety, and U.S. patent application Ser. No. 13/152,119, Jun. 2, 2011, entitled “SENSORY INPUT PROCESSING APPARATUS AND METHODS”, incorporated supra.
Typically, artificial spiking neural networks, such as the exemplary network described in owned U.S. patent application Ser. No. 13/541,531, entitled “CONDITIONAL PLASTICITY SPIKING NEURON NETWORK APPARATUS AND METHODS”, incorporated supra, may comprise a plurality of units (or nodes), which can be thought of as corresponding to neurons in a biological neural network. Any given unit may be connected to many other units via connections, also referred to as communications channels, and/or synaptic connections. The units providing inputs to any given unit are commonly referred to as the pre-synaptic units, while the units receiving the inputs are referred to as the post-synaptic units.
Individual ones of the unit-to-unit connections may be assigned, inter cilia, a connection efficacy, which in general may refer to a magnitude and/or probability of input spike influence on unit output response (i.e., output spike generation/firing). The efficacy may comprise, for example a parameter (e.g., synaptic weight) by which one or more state variables of post-synaptic unit are changed. The efficacy may comprise a latency parameter by characterizing propagation delay from a pre-synaptic unit to a post-synaptic unit. In some implementations, greater efficacy may correspond to a shorter latency.
Some existing implementations of temporal learning (e.g., slow feature analysis) by spiking neural networks via spike timing dependent plasticity and/or increased excitability may develop diminished responsiveness (‘forget’) features that did not appear for an extended period of time (e.g., 10 minutes or longer for a 25 frames per second (fps) visual stimulus input).
Previously strong but presently inactive input synapses may become depressed based on the activity of the post synaptic neuron. This configuration may lead (especially in multi-layer processing networks) to unstable input synaptic sets and/or receptive fields.
Accordingly, there is a salient need for improved network operation capable of, inter alia, responding efficiently to stimuli that may appear at long intervals between one another.